brave-mcp-langchain
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@brave-mcp-langchainsearch for LangGraph overview and fetch its content"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
brave-mcp-langchain
Create venv
uv syncRelated MCP server: Brave Search MCP Server
Install package
uv pip install brave-mcp-langchainRun MCP server in STDIO mode
uvx brave-mcp-langchainTo run MCP server in SSE mode
uvx brave-mcp-langchain sse 5003MCP Setting
{
"mcpServers": {
"brave-mcp-langchain": {
"disabled": false,
"timeout": 60,
"type": "stdio",
"command": "uvx",
"args": [
"brave-mcp-langchain"
]
}
}
}Use as Langchain tool
It can also be used as Langchain tool. Below is how to validate tool.
import httpx
import asyncio
from langchain.tools import Tool
from brave_mcp_langchain import brave_tool
async def test_search():
result = await brave_tool.search_tool.ainvoke({"query": "LangGraph overview", "max_results": 10})
print(result)
result = await brave_tool.fetch_content_tool.ainvoke({
"url": "https://iamatulsingh.github.io"
})
print(result)
asyncio.run(test_search())Use with langchain example
import asyncio
from langchain.agents import initialize_agent
from langchain.agents.agent_types import AgentType
from langchain_ollama import ChatOllama
from brave_mcp_langchain import brave_tool
llm = ChatOllama(model="llama3.1:8b")
tools = [
brave_tool.search_tool,
brave_tool.fetch_content_tool
]
agent = initialize_agent(
tools=[brave_tool.search_tool, brave_tool.fetch_content_tool],
llm=llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
async def run_agent_query():
response = await agent.ainvoke(
"Search for 'iamatulsingh' overview, then fetch content from https://iamatulsingh.github.io"
)
print("\nAgent Response:")
print(response)
asyncio.run(run_agent_query())🧠Inspiration & Attribution
This project, brave-mcp-langchain, was inspired by and partially based on the excellent work in duckduckgo-mcp-server by @nickclyde. That project laid the groundwork for integrating DuckDuckGo search and content fetching into the MCP ecosystem.
While brave-mcp-langchain extends the concept to support Brave Search and LangChain workflows, several architectural ideas and implementation patterns were adapted from duckduckgo-mcp-server, which is licensed under the MIT License.
I'm grateful for the open-source community and contributors who make projects like this possible. If you’re interested in DuckDuckGo-based search tools, definitely check out the original repository!
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